[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124510-en":3,"doc-seo-124510-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124510,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Estimation of Rock Brittleness from Point Load Strength Index Data Using Machine Learning Methods","Brittleness is a key mechanical property describing a rock’s tendency to fracture under stress with limited deformation, with strong relevance for mining, tunnelling, and broader geotechnical engineering. Accurate brittleness prediction supports safer excavation design and improves the cost efficiency of resource extraction. Conventional assessments relying on UCS and tensile strength are often labour-intensive, slow, and expensive. This work proposes a machine-learning prediction framework using Point Load Strength Index (PLI) as the sole input, trained on a dataset of sedimentary, igneous, and metamorphic rocks. Multiple regression models and ensemble learners are compared under a unified evaluation, where Gradient Boosting Regressor achieves high accuracy (R² up to 0.96 for metamorphic rocks).","ISSN 1330-3651(Print), ISSN 1848-6339 (Online) [https://doi.org/10.17559/TV-20250507002651](https://doi.org/10.17559/TV-20250507002651)  \n[Received: 7 May 2025](Received: 7 May 2025); Accepted: 20 November 2025 Original scientific paper  \nEstimation of Rock Brittleness from Point Load Strength Index Data Using Machine  \nLearning Methods  \nDeniz AKBAY*, Gökhan EKINCIOGLU, Murat ISIK, Mehmet Ali YALCINKAYA  \nAbstract: Brittleness is a vital mechanical property that characterizes a rock's tendency to fracture under applied stress without significant deformation, which is particularly significant in mining, tunnelling, and other geotechnical engineering applications. The accurate prediction of rock brittleness is essential for optimizing excavation strategies, ensuring operational safety, and improving the cost-efficiency of resource extraction processes. However, conventional brittleness assessment techniques-such as those based on uniaxial compressive strength (UCS) and tensile strength-can be labour-intensive, time-consuming, and expensive. This study introduces a predictive framework based on machine learning algorithms using Point Load Strength Index (PLI) values as the sole input variable. A comprehensive dataset comprising sedimentary, igneous, and metamorphic rocks was compiled from both literature sources and laboratory experiments. Multiple regression models were applied and compared, including traditional linear methods and advanced ensemble learners. Among these, the Gradient Boosting Regressor delivered the highest predictive accuracy, achieving an (R²) value of 0.96 for metamorphic rocks. The results demonstrate that even a single indirect measurement like PLI can serve as an effective predictor of rock brittleness when coupled with robust machine learning techniques. The findings highlight the potential of integrating AI-based models into rock mechanics workflows to streamline brittleness estimation and support sustainable mining practices.  \nKeywords: geotechnical engineering; machine learning; non-destructive testing; point load strength index; rock brittleness  \n1 INTRODUCTION  \nIn rock mechanics and geotechnical applications, brittleness is a crucial mechanical parameter that defines a materialꞌs sensitivity to sudden failure without significant deformation. Several researchers have so far put forth alternative ways to measure rock brittleness based on various ideas, taking into account a variety of influencing elements such as strength parameters, in-situ stress, and mineral composition. Each index's dependability depends on whether the right strategy is applied for the intended use [1] .  \nBrittleness is the ability of a material to continuously deform, fracture, or crack under force without undergoing permanent deformations prior to failure [2] .  \nThe brittleness of rock significantly influences its mechanical behavior and failure characteristics, particularly in applications involving rock excavation, such as drillability and cuttability [3-7], fracability during hydraulic fracturing [8-13], and rockburst proneness in deep hard rock tunnels [14] . Reliable evaluation of rock brittleness is essential for several critical engineering tasks, including the appropriate design and selection of excavation and mining equipment, the stimulation of unconventional shale gas formations, and the stability analysis of deep hard rock tunnels.  \nThe brittleness values of rocks are typically determined in engineering applications using the tensile strength (TS) and uniaxial compressive strength (UCS) of the rock [6, 14-23] . The motivation of the study is that conventional brittleness assessment based on UCS and TS requires specimen preparation and specialized equipment, making testing costly and time-consuming. In practice, indirect tests such as the Point Load Strength Index (PLI) are favoured because they are simpler and faster. The research question of the study is whether the B3 brittleness index of intact rocks can be","cbCaiapeYtz40v4p","https://ap.wps.com/l/cbCaiapeYtz40v4p","pdf",1826540,1,13,"English","en",105,"# Introduction\n## Research motivation and problem statement\n## Contributions and modeling strategy\n# Related Works","[{\"question\":\"为什么需要预测岩石脆性？\",\"answer\":\"脆性决定岩石在开挖等工程中的失效敏感性，影响钻进性、切削性、压裂可裂性以及深部硬岩隧道的岩爆倾向。可靠评估对设备选择、非常规页岩气刺激及隧道稳定分析都很关键。\"},{\"question\":\"传统脆性评估方法有哪些局限？\",\"answer\":\"基于抗拉强度和单轴抗压强度的评估通常需要试样制备与专用设备，导致测试成本高、耗时长。\"},{\"question\":\"研究如何用 PLI 单一输入来预测脆性？\",\"answer\":\"研究汇编并统一了包含 520 个样本的跨文献数据集，使用 PLI 作为唯一预测变量（对应 B3 脆性指标），并对多类回归与集成算法在统一的交叉验证方案下进行对比。\"},{\"question\":\"哪种机器学习模型表现最好？\",\"answer\":\"在比较中，Gradient Boosting Regressor 的预测精度最高，针对变质岩可达到最高 R²=0.96。\"}]","Estimation of Rock Brittleness from Point Load Strength Index Data Using Machine Learning Methods | PDF",1785822832,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"estimation-of-rock-brittleness-from-point-load-strength-index-data-using-machine-learning-methods","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/estimation-of-rock-brittleness-from-point-load-strength-index-data-using-machine-learning-methods/124510/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要预测岩石脆性？","Question",{"text":75,"@type":76},"脆性决定岩石在开挖等工程中的失效敏感性，影响钻进性、切削性、压裂可裂性以及深部硬岩隧道的岩爆倾向。可靠评估对设备选择、非常规页岩气刺激及隧道稳定分析都很关键。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"传统脆性评估方法有哪些局限？",{"text":80,"@type":76},"基于抗拉强度和单轴抗压强度的评估通常需要试样制备与专用设备，导致测试成本高、耗时长。",{"name":82,"@type":73,"acceptedAnswer":83},"研究如何用 PLI 单一输入来预测脆性？",{"text":84,"@type":76},"研究汇编并统一了包含 520 个样本的跨文献数据集，使用 PLI 作为唯一预测变量（对应 B3 脆性指标），并对多类回归与集成算法在统一的交叉验证方案下进行对比。",{"name":86,"@type":73,"acceptedAnswer":87},"哪种机器学习模型表现最好？",{"text":88,"@type":76},"在比较中，Gradient Boosting Regressor 的预测精度最高，针对变质岩可达到最高 R²=0.96。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]